Codebook - Stichprobe Prototypenansatz.sav

knitr::opts_chunk$set(
  warning = FALSE, # show warnings during codebook generation
  message = FALSE, # show messages during codebook generation
  error = TRUE, # do not interrupt codebook generation in case of errors,
                # usually better for debugging
  echo = TRUE  # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())
pander::panderOptions("table.split.table", Inf)

We collected the following data.

# omit the following lines, if your missing values are already properly labelled
codebook_data <- detect_missing(codebook_data,
    only_labelled = TRUE, # only labelled values are autodetected as
                                   # missing
    negative_values_are_missing = FALSE, # negative values are missing values
    ninety_nine_problems = TRUE,   # 99/999 are missing values, if they
                                   # are more than 5 MAD from the median
    )

# If you are not using formr, the codebook package needs to guess which items
# form a scale. The following line finds item aggregates with names like this:
# scale = scale_1 + scale_2R + scale_3R
# identifying these aggregates allows the codebook function to
# automatically compute reliabilities.
# However, it will not reverse items automatically.
codebook_data <- detect_scales(codebook_data)


# Does your dataset have a name that is not reflected in the file name?
# Uncomment the line below and change the name
# metadata(codebook_data)$name <- "My Awesome Dataset"

codebook(codebook_data)

Metadata

Description

Dataset name: codebook_data

The dataset has N=123 rows and 3 columns. 123 rows have no missing values on any column.

Metadata for search engines

• Date published: 2025-05-09

x Geschlecht Bildungsniveau Alter

#Variables

Geschlecht

1

Distribution

Distribution of values for Geschlecht

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	format.spss	display_width	label
Geschlecht	numeric	0	1	1	1	2	1.195122	0.3979154	▇▁▁▁▂	F8.0	13	NA

Value labels

Response choices

name	value
weiblich	1
maennlich	2

Bildungsniveau

1

Distribution

Distribution of values for Bildungsniveau

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	format.spss	display_width	label
Bildungsniveau	numeric	0	1	1	1	2	1.00813	0.090167	▇▁▁▁▁	F8.0	13	NA

Value labels

Response choices

name	value
Abitur	1
Kein_Abitur	2

Alter

Distribution

Distribution of values for Alter

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	format.spss	display_width	label
Alter	numeric	0	1	19	22	49	24.78049	6.605433	▇▁▁▁▁	F8.0	11	NA

Missingness report

Codebook table

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "codebook_data",
  "datePublished": "2025-05-09",
  "description": "The dataset has N=123 rows and 3 columns.\n123 rows have no missing values on any column.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name           |label | n_missing|\n|:--------------|:-----|---------:|\n|Geschlecht     |NA    |         0|\n|Bildungsniveau |NA    |         0|\n|Alter          |NA    |         0|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "keywords": ["Geschlecht", "Bildungsniveau", "Alter"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "Geschlecht",
      "value": "1. weiblich,\n2. maennlich",
      "maxValue": 2,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "Bildungsniveau",
      "value": "1. Abitur,\n2. Kein_Abitur",
      "maxValue": 2,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "Alter",
      "@type": "propertyValue"
    }
  ]
}`